'''simple docstring'''




import unittest

from transformers import LiltConfig, is_torch_available
from transformers.testing_utils import require_torch, slow, torch_device

from ...generation.test_utils import GenerationTesterMixin
from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
  import torch

  from transformers import (
      LiltForQuestionAnswering,
      LiltForSequenceClassification,
      LiltForTokenClassification,
      LiltModel,
  )
  from transformers.models.lilt.modeling_lilt import LILT_PRETRAINED_MODEL_ARCHIVE_LIST




class        a__ :






     def __init__(					self						:						Optional[int]    ,							a						:						List[str]    ,							a						:						Dict=13    ,							a						:						Union[str, Any]=7    ,							a						:						List[Any]=True    ,							a						:						Dict=True    ,							a						:						Dict=True    ,							a						:						Optional[Any]=True    ,							a						:						str=99    ,							a						:						Union[str, Any]=24    ,							a						:						int=2    ,							a						:						int=6    ,							a						:						Union[str, Any]=37    ,							a						:						Tuple="gelu"    ,							a						:						Tuple=0.1    ,							a						:						Any=0.1    ,							a						:						Union[str, Any]=5_12    ,							a						:						str=16    ,							a						:						int=2    ,							a						:						Union[str, Any]=0.02    ,							a						:						int=3    ,							a						:						Union[str, Any]=None    ,							a						:						Any=10_00    ,							):





         """simple docstring"""



         __lowerCamelCase            =    parent
         __lowerCamelCase            =    batch_size
         __lowerCamelCase            =    seq_length
         __lowerCamelCase            =    is_training
         __lowerCamelCase            =    use_input_mask
         __lowerCamelCase            =    use_token_type_ids
         __lowerCamelCase            =    use_labels
         __lowerCamelCase            =    vocab_size
         __lowerCamelCase            =    hidden_size
         __lowerCamelCase            =    num_hidden_layers
         __lowerCamelCase            =    num_attention_heads
         __lowerCamelCase            =    intermediate_size
         __lowerCamelCase            =    hidden_act
         __lowerCamelCase            =    hidden_dropout_prob
         __lowerCamelCase            =    attention_probs_dropout_prob
         __lowerCamelCase            =    max_position_embeddings
         __lowerCamelCase            =    type_vocab_size
         __lowerCamelCase            =    type_sequence_label_size
         __lowerCamelCase            =    initializer_range
         __lowerCamelCase            =    num_labels
         __lowerCamelCase            =    scope
         __lowerCamelCase            =    range_bbox






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    ids_tensor([self.batch_size, self.seq_length]    ,							self.vocab_size       )

         __lowerCamelCase            =    ids_tensor([self.batch_size, self.seq_length, 4]    ,							self.range_bbox       )
         # Ensure that bbox is legal
         for i in range(bbox.shape[0]       ):
             for j in range(bbox.shape[1]       ):
                 if bbox[i, j, 3] < bbox[i, j, 1]:
                     __lowerCamelCase            =    bbox[i, j, 3]
                     __lowerCamelCase            =    bbox[i, j, 1]
                     __lowerCamelCase            =    t
                 if bbox[i, j, 2] < bbox[i, j, 0]:
                     __lowerCamelCase            =    bbox[i, j, 2]
                     __lowerCamelCase            =    bbox[i, j, 0]
                     __lowerCamelCase            =    t

         __lowerCamelCase            =    None
         if self.use_input_mask:
             __lowerCamelCase            =    ids_tensor([self.batch_size, self.seq_length]    ,							vocab_size=2       )

         __lowerCamelCase            =    None
         if self.use_token_type_ids:
             __lowerCamelCase            =    ids_tensor([self.batch_size, self.seq_length]    ,							self.type_vocab_size       )

         __lowerCamelCase            =    None
         __lowerCamelCase            =    None
         if self.use_labels:
             __lowerCamelCase            =    ids_tensor([self.batch_size]    ,							self.type_sequence_label_size       )
             __lowerCamelCase            =    ids_tensor([self.batch_size, self.seq_length]    ,							self.num_labels       )

         __lowerCamelCase            =    self.get_config()

         return config, input_ids, bbox, token_type_ids, input_mask, sequence_labels, token_labels






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         return LiltConfig(
             vocab_size=self.vocab_size    ,							hidden_size=self.hidden_size    ,							num_hidden_layers=self.num_hidden_layers    ,							num_attention_heads=self.num_attention_heads    ,							intermediate_size=self.intermediate_size    ,							hidden_act=self.hidden_act    ,							hidden_dropout_prob=self.hidden_dropout_prob    ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob    ,							max_position_embeddings=self.max_position_embeddings    ,							type_vocab_size=self.type_vocab_size    ,							initializer_range=self.initializer_range    ,							)






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]    ,							a						:						Any    ,							a						:						Any    ,							a						:						Any    ,							a						:						Dict    ,							a						:						List[str]    ,							a						:						Tuple    ,							a						:						Dict    ,							):





         """simple docstring"""



         __lowerCamelCase            =    LiltModel(config=a       )
         model.to(a       )
         model.eval()
         __lowerCamelCase            =    model(a    ,							bbox=a    ,							attention_mask=a    ,							token_type_ids=a       )
         __lowerCamelCase            =    model(a    ,							bbox=a    ,							token_type_ids=a       )
         __lowerCamelCase            =    model(a    ,							bbox=a       )

         self.parent.assertEqual(result.last_hidden_state.shape    ,							(self.batch_size, self.seq_length, self.hidden_size)       )
         self.parent.assertEqual(result.pooler_output.shape    ,							(self.batch_size, self.hidden_size)       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int    ,							a						:						int    ,							a						:						Tuple    ,							a						:						Dict    ,							a						:						List[Any]    ,							a						:						Union[str, Any]    ,							a						:						Optional[Any]    ,							a						:						List[str]    ,							):





         """simple docstring"""



         __lowerCamelCase            =    self.num_labels
         __lowerCamelCase            =    LiltForTokenClassification(config=a       )
         model.to(a       )
         model.eval()
         __lowerCamelCase            =    model(
             a    ,							bbox=a    ,							attention_mask=a    ,							token_type_ids=a    ,							labels=a       )
         self.parent.assertEqual(result.logits.shape    ,							(self.batch_size, self.seq_length, self.num_labels)       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						Union[str, Any]    ,							a						:						Optional[Any]    ,							a						:						Optional[int]    ,							a						:						List[str]    ,							a						:						str    ,							a						:						int    ,							a						:						Union[str, Any]    ,							):





         """simple docstring"""



         __lowerCamelCase            =    LiltForQuestionAnswering(config=a       )
         model.to(a       )
         model.eval()
         __lowerCamelCase            =    model(
             a    ,							bbox=a    ,							attention_mask=a    ,							token_type_ids=a    ,							start_positions=a    ,							end_positions=a    ,							)
         self.parent.assertEqual(result.start_logits.shape    ,							(self.batch_size, self.seq_length)       )
         self.parent.assertEqual(result.end_logits.shape    ,							(self.batch_size, self.seq_length)       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    self.prepare_config_and_inputs()
         (
             (
             __lowerCamelCase
         )      ,    (
             __lowerCamelCase
         )      ,    (
             __lowerCamelCase
         )      ,    (
             __lowerCamelCase
         )      ,    (
             __lowerCamelCase
         )      ,    (
             __lowerCamelCase
         )      ,    (
             __lowerCamelCase
         )      ,    
         )            =    config_and_inputs
         __lowerCamelCase            =    {
             '''input_ids''': input_ids,
             '''bbox''': bbox,
             '''token_type_ids''': token_type_ids,
             '''attention_mask''': input_mask,
         }
         return config, inputs_dict




@require_torch
class        a__ (  UpperCAmelCase__				,  UpperCAmelCase__				,  UpperCAmelCase__				,  unittest.TestCase					):
     lowerCamelCase    :	Any				=(
         (
             LiltModel,
             LiltForSequenceClassification,
             LiltForTokenClassification,
             LiltForQuestionAnswering,
         )
         if is_torch_available()
         else ()
     )
     lowerCamelCase    :	Union[str, Any]				=(
         {
             "feature-extraction": LiltModel,
             "question-answering": LiltForQuestionAnswering,
             "text-classification": LiltForSequenceClassification,
             "token-classification": LiltForTokenClassification,
             "zero-shot": LiltForSequenceClassification,
         }
         if is_torch_available()
         else {}
     )
     lowerCamelCase    :	Any				=False
     lowerCamelCase    :	Optional[Any]				=False






     def   SCREAMING_SNAKE_CASE__  (					self						:						Tuple    ,							a						:						str    ,							a						:						Optional[int]    ,							a						:						Any    ,							a						:						Optional[int]    ,							a						:						Optional[int]       ):





         """simple docstring"""



         return True






     def   SCREAMING_SNAKE_CASE__  (					self						:						Dict       ):





         """simple docstring"""



         __lowerCamelCase            =    LiltModelTester(self       )
         __lowerCamelCase            =    ConfigTester(self    ,							config_class=a    ,							hidden_size=37       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[Any]       ):





         """simple docstring"""



         self.config_tester.run_common_tests()






     def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_model(*a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.model_tester.prepare_config_and_inputs()
         for type in ["absolute", "relative_key", "relative_key_query"]:
             __lowerCamelCase            =    type
             self.model_tester.create_and_check_model(*a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         __lowerCamelCase            =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_token_classification(*a       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.model_tester.prepare_config_and_inputs()
         self.model_tester.create_and_check_for_question_answering(*a       )






     @slow
     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         for model_name in LILT_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
             __lowerCamelCase            =    LiltModel.from_pretrained(a       )
             self.assertIsNotNone(a       )




@require_torch
@slow
class        a__ (  unittest.TestCase					):






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    LiltModel.from_pretrained('''SCUT-DLVCLab/lilt-roberta-en-base'''       ).to(a       )

         __lowerCamelCase            =    torch.tensor([[1, 2]]    ,							device=a       )
         __lowerCamelCase            =    torch.tensor([[[1, 2, 3, 4], [5, 6, 7, 8]]]    ,							device=a       )

         # forward pass
         with torch.no_grad():
             __lowerCamelCase            =    model(input_ids=a    ,							bbox=a       )

         __lowerCamelCase            =    torch.Size([1, 2, 7_68]       )
         __lowerCamelCase            =    torch.tensor(
             [[-0.06_53, 0.09_50, -0.00_61], [-0.05_45, 0.09_26, -0.03_24]]    ,							device=a    ,							)

         self.assertTrue(outputs.last_hidden_state.shape    ,							a       )
         self.assertTrue(torch.allclose(outputs.last_hidden_state[0, :, :3]    ,							a    ,							atol=1e-3       )       )

'''simple docstring'''




import timeit

import numpy as np

import datasets
from datasets.arrow_writer import ArrowWriter
from datasets.features.features import _ArrayXD






def __lowerCAmelCase					(				UpperCamelCase__							)      ->       List[str]:
    def wrapper(*UpperCamelCase__							,    **UpperCamelCase__							):
        __lowerCamelCase            =    timeit.default_timer()
        __lowerCamelCase            =    func(*UpperCamelCase__							,    **UpperCamelCase__							)
        __lowerCamelCase            =    timeit.default_timer() - starttime
        return delta

    __lowerCamelCase            =    func.__name__

    return wrapper






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    []
    __lowerCamelCase            =    seq_shapes or {}
    for i in range(UpperCamelCase__							):
        __lowerCamelCase            =    {}
        for col_id, (k, v) in enumerate(features.items()							):
            if isinstance(UpperCamelCase__							,    _ArrayXD							):
                __lowerCamelCase            =    np.random.rand(*v.shape							).astype(v.dtype							)
            elif isinstance(UpperCamelCase__							,    datasets.Value							):
                if v.dtype == "string":
                    __lowerCamelCase            =    '''The small grey turtle was surprisingly fast when challenged.'''
                else:
                    __lowerCamelCase            =    np.random.randint(10							,    size=1							).astype(v.dtype							).item()
            elif isinstance(UpperCamelCase__							,    datasets.Sequence							):
                while isinstance(UpperCamelCase__							,    datasets.Sequence							):
                    __lowerCamelCase            =    v.feature
                __lowerCamelCase            =    seq_shapes[k]
                __lowerCamelCase            =    np.random.rand(*UpperCamelCase__							).astype(v.dtype							)
            __lowerCamelCase            =    data

        dummy_data.append((i, example)							)

    return dummy_data






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__=1_00							,    UpperCamelCase__=None							)      ->       Dict:
    __lowerCamelCase            =    generate_examples(UpperCamelCase__							,    num_examples=UpperCamelCase__							,    seq_shapes=UpperCamelCase__							)

    with ArrowWriter(features=UpperCamelCase__							,    path=UpperCamelCase__							) as writer:
        for key, record in dummy_data:
            __lowerCamelCase            =    features.encode_example(UpperCamelCase__							)
            writer.write(UpperCamelCase__							)

        __lowerCamelCase      ,    __lowerCamelCase            =    writer.finalize()

    if not num_final_examples == num_examples:
        raise ValueError(
            f"""Error writing the dataset, wrote {num_final_examples} examples but should have written {num_examples}."""							)

    __lowerCamelCase            =    datasets.Dataset.from_file(filename=UpperCamelCase__							,    info=datasets.DatasetInfo(features=UpperCamelCase__							)							)

    return dataset
